import torch
import torch.nn.functional as F
import numpy as np
from math import exp, sqrt


def gaussian(window_size, sigma):
    gauss = torch.Tensor(
        [
            exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2))
            for x in range(window_size)
        ]
    )
    return gauss / gauss.sum()


def create_window(window_size, channel, std=1.5):
    _1D_window = gaussian(window_size, std).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).unsqueeze(0).unsqueeze(0)
    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
    return window


def _ssim(pred, gt, window, channel):
    ntotpx, nviews, nc, h, w = pred.shape
    flat_pred = pred.view(-1, nc, h, w)
    mu1 = F.conv2d(flat_pred, window, padding=0, groups=channel).view(
        ntotpx, nviews, nc
    )
    mu2 = F.conv2d(gt, window, padding=0, groups=channel).view(ntotpx, nc)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2).unsqueeze(1)
    mu1_mu2 = mu1 * mu2.unsqueeze(1)

    sigma1_sq = (
        F.conv2d(flat_pred * flat_pred, window, padding=0, groups=channel).view(
            ntotpx, nviews, nc
        )
        - mu1_sq
    )
    sigma2_sq = (
        F.conv2d(gt * gt, window, padding=0, groups=channel).view(ntotpx, 1, 3) - mu2_sq
    )
    sigma12 = (
        F.conv2d(
            (pred * gt.unsqueeze(1)).view(-1, nc, h, w),
            window,
            padding=0,
            groups=channel,
        ).view(ntotpx, nviews, nc)
        - mu1_mu2
    )

    C1 = 0.01**2
    C2 = 0.03**2

    values = 1 - ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
        (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
    )
    return torch.sum(values, dim=2) / 2


class SSIM(torch.nn.Module):
    def __init__(self, h_patch_size):
        super(SSIM, self).__init__()
        self.window_size = 2 * h_patch_size + 1
        self.channel = 3
        self.register_buffer("window", create_window(self.window_size, self.channel))

    def forward(self, img_pred, img_gt):
        ntotpx, nviews, npatch, channels = img_pred.shape

        patch_size = int(sqrt(npatch))
        patch_img_pred = (
            img_pred.reshape(ntotpx, nviews, patch_size, patch_size, channels)
            .permute(0, 1, 4, 2, 3)
            .contiguous()
        )
        patch_img_gt = img_gt.reshape(ntotpx, patch_size, patch_size, channels).permute(
            0, 3, 1, 2
        )

        return _ssim(patch_img_pred, patch_img_gt, self.window, self.channel)
